Fig 3. Spatial decoding across animals in 2D and 1D environments

This code example is a Jupyter notebook with Script of Scripts (SoS) workflow. It reproduces interactive figures for the paper by first author Ardi Tampuu and last author Raul Vicente.

The calculations are written using Python 2.7 (from this fetched from repo), and the interactive figures are written in Python 3.6 with the ploting library Plotly.

Figure 3:

(a-b) Decoding results in a 1m square environment. The RNN consistently outperforms the two Bayesian approaches in all 5 data sets. Mean and median errors across cross validation folds, respectively. (c-d) Decoding errors from a 600 cm long Z-shaped track. RNN consistently yields lower decoding errors than the Bayesian approaches, the difference is more marked when mean (c) as oppose to median (d) errors are considered.

https://doi.org/10.1371/journal.pcbi.1006822.g003

Python 2.7

Figure with calculations in Python 2.7 Matplotlib:

2D decoding task

Figure 3.a, Matplotlib:

%use Python2
import matplotlib.pyplot as plt
import matplotlib.style
import matplotlib as mpl
mpl.style.use('classic')
import numpy as np
from scipy.io import loadmat
%matplotlib inline


#set up nicer color scheme
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),  
             (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),  
             (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),  
             (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),  
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Rescale to values between 0 and 1 
for i in range(len(tableau20)):  
    r, g, b = tableau20[i]  
    tableau20[i] = (r / 255., g / 255., b / 255.)
    
# this file contains the perfromance of 10 models on R2192 "grep"-ed from the actual log files
f=open("R2192_grepped_predictions.log")
lines = f.readlines()
f.close()

# fill a dictionary where keys are timewindow sizes, filled with [mean, median] for 10 models
RNN_stats={}
for line in lines:
    pieces=line.split(" ")
    win_size = pieces[0].split("x")[1]
    win_size = int(win_size[:win_size.find("_")])
    median = float(pieces[-1])
    mean = float(pieces[-5][:-1])
    if win_size in RNN_stats.keys():
        RNN_stats[win_size].append([mean, median])
    else:
        RNN_stats[win_size]=[[mean, median]]
# print RNN_stats[1400]

# Results with Bayes with flat prior (MLE)
# imported as a dictionary, each item in dictionary contains results for all 5 rats
# first rat is R2192 (index 0)
bay_field_dict = loadmat("Bayes_res/Fig1&3ab_decodingDataForOpenField.mat")
#bay_field_dict = loadmat("Bayes_res/2dDecodeAllRatsAllWindowsNaiveBayesNoTruncate.mat")
# print bay_field_dict.keys()
flat_medians=bay_field_dict['medianErr']
flat_means= bay_field_dict['meanErr']
flat_win = bay_field_dict['tWin2Test'].flatten()
# print flat_win.shape, flat_means[:,0].shape
# print "\n for Table 1: ", bay_field_dict["bstMean"]

# """ for Table 1:  [[ 15.82963073  16.06929415  17.86089428  18.81734775  17.03691594]
#  [  2.8          3.8          2.8          2.8          3.4       ]]"""

# Results with Bayes with flat prior (MLE)
# imported as a dictionary, each item in dictionary contains results for all 5 rats
# first rat is R2192 (index 0)

# bay_field_dict_history_h5 = loadmat("Bayes_res/oldParams_2dDecodeFullBayesWithHistorySigma1History5.mat") #old params
bay_field_dict_history = loadmat("Bayes_res/Fig1&3ab_2dDecodeFullBayesWithHistorySigma1History15.mat")
#bay_field_dict_history = loadmat("Bayes_res/2dDecodeAllRatsAllWindowsBayesWithHistorySpeed1History15NoTruncate.mat")
# print bay_field_dict_history.keys()
memory_medians = bay_field_dict_history['medianErr']
memory_means = bay_field_dict_history['meanErr']
memory_win = bay_field_dict_history['tWin2Test'].flatten()

# print memory_means[:,0]
# print memory_medians[:,0]
# print np.min(memory_medians, axis=0)

# print "\n for Table 1: ", bay_field_dict_history["bstMean"]
# """ for Table 1:  [[ 15.46168191  14.99576142  16.5269506   18.26098815  16.40828295]
#  [  2.           1.8          2.8          2.6          3.4       ]]"""

labels =["R2192", "R2198","R2336","R2337", "R2217"]
order_of_animals = [0,1,3,4,2]
# results from RNN exctracted from log files and averaged
means = [12.50484, 13.27721, 16.28584545, 14.36655,  14.4936]
mean_std = [0.3846042705, 0.3226775496, 0.269230256, 0.2945978247,0.2544971159]

medians = [10.3296,10.75701,13.09772, 11.3265, 11.73398]
median_std =[0.23188, 0.31606,0.19988, 0.15216, 0.24249]

# Results from flat Bayes (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as it has least neurons
bayes =  np.min(flat_means, axis=0)[order_of_animals] #[ 15.82963073,  16.06929415, 18.81734775,  17.03691594,  17.86089428 ]
bayes_med =  np.min(flat_medians, axis=0)[order_of_animals]#[ 12.,   12.64911064 ,   14. ,  12.16552506, 14.14213562]

# Results from Bayes with memory (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as has least neurons
memory_bayes =  np.min(memory_means, axis=0)[order_of_animals] #[ 15.46168191,  14.99576142,  18.26098815,  16.40828295, 16.5269506] 
memory_bayes_med = np.min(memory_medians, axis=0)[order_of_animals] #[ 11.3137085,  12., 13.11132817,  12.16552506, 12.64911064]

def autolabel(rects):
    # attach some text labels
    for i,rect in enumerate(rects):
        height = rect.get_height()
        col=tableau20[0]
        if height in memory_bayes:
            col="black"
            ax.text(rect.get_x() + rect.get_width()/2., 1.0*height,
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)
        elif height in bayes:
            col=tableau20[6]
            ax.text(rect.get_x() + rect.get_width()/2., 1.025*height+0.5,
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)
        else:
            ax.text(rect.get_x() + rect.get_width()/2., 1.05*height+mean_std[i],
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)

#plot Bayes_flat, Bayes_memory and RNN
fig, ax = plt.subplots(figsize=(8,6))
indx=np.arange(5)
width=0.3
# RNN
rects1 = ax.bar(indx, means, width, color=tableau20[0], yerr=mean_std, error_kw={"linewidth":4, "ecolor":"black", "capsize":8,"capthick":2})
# Bayes mem
rects2 = ax.bar(indx + width+0.08, memory_bayes, width/2, color=tableau20[6], hatch='///')
# Bayes flat
rects3 = ax.bar(indx + width*1.5+0.09, bayes, width/2, color=tableau20[6])

# add some text for labels, title and axes ticks
plt.ylabel('Mean error (cm)',fontsize=22)
ax.set_xticks(indx + width+0.025)
ax.set_xticklabels(labels,fontsize=22)
ax.legend((rects1[0], rects2[0], rects3[0]), ('RNN',"Bayesian memory", "MLE"),ncol=3,fontsize=18)

plt.yticks(fontsize=20)
plt.ylim([0,24.5])
plt.xlim([-0.1,4.78])
plt.title("(a)   Mean errors in 2D decoding task",fontsize=24,y=1.08, x=0.45)

plt.tight_layout()

autolabel(rects1)
autolabel(rects2)
autolabel(rects3)

# plt.savefig("mean_erors.eps",format='eps')
# plt.savefig("mean_errors.png",format='png')
plt.show()

Figure 3.b, Matplotlib:

%use Python2
import matplotlib.pyplot as plt
import matplotlib.style
import matplotlib as mpl
mpl.style.use('classic')
import numpy as np
from scipy.io import loadmat
%matplotlib inline

labels =["R2192", "R2198","R2336","R2337", "R2217"]
#results from RNN
means = [12.50484, 13.27721, 16.28584545, 14.36655,  14.4936]
mean_std = [0.3846042705, 0.3226775496, 0.269230256, 0.2945978247,0.2544971159]
medians = [10.3296,10.75701,13.09772, 11.3265, 11.73398]
median_std =[0.23188, 0.31606,0.19988, 0.15216, 0.24249]

# Results from flat Bayes (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as it has least neurons
bayes =  np.min(flat_means, axis=0)[order_of_animals] #[ 15.82963073,  16.06929415, 18.81734775,  17.03691594,  17.86089428 ]
bayes_med =  np.min(flat_medians, axis=0)[order_of_animals]#[ 12.,   12.64911064 ,   14. ,  12.16552506, 14.14213562]

# Results from Bayes with memory (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as has least neurons
memory_bayes =  np.min(memory_means, axis=0)[order_of_animals] #[ 15.46168191,  14.99576142,  18.26098815,  16.40828295, 16.5269506] 
memory_bayes_med = np.min(memory_medians, axis=0)[order_of_animals] #[ 11.3137085,  12., 13.11132817,  12.16552506, 12.64911064]


def autolabel(rects):
    # attach some text labels
    for i,rect in enumerate(rects):
        height = rect.get_height()
        col=tableau20[0]
        if height in memory_bayes_med:
            col="black"
            if height== 13.1113:
                ax.text(rect.get_x() + rect.get_width()/2., 1.02*height+0.4,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            else:
                ax.text(rect.get_x() + rect.get_width()/2., 1.02*height,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
        elif height in bayes_med:
            col=tableau20[6]
            if height== 12.16552506:
                ax.text(rect.get_x() + rect.get_width()/2., 1.0*height+1.6,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            else:
                ax.text(rect.get_x() + rect.get_width()/2., 1.0*height+0.6,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
        else:
            ax.text(rect.get_x() + rect.get_width()/2., 1.0*height+median_std[i],
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)
            
#plot Bayes_flat, Bayes_memory and RNN
fig, ax = plt.subplots(figsize=(8,6))
indx=np.arange(5)
width=0.3
# RNN, flat, memory
rects1 = ax.bar(indx, medians, width, color=tableau20[0], yerr=median_std, error_kw={"linewidth":4, "ecolor":"black", "capsize":8,"capthick":2})
rects2 = ax.bar(indx + width+0.08, memory_bayes_med, width/2, color=tableau20[6], hatch="///")
rects3 = ax.bar(indx + width*1.5+0.09, bayes_med, width/2, color=tableau20[6])

# add some text for labels, title and axes ticks
plt.ylabel('Median error (cm)',fontsize=22)
ax.set_xticks(indx + width+0.025)
ax.set_xticklabels(labels,fontsize=22)

ax.legend((rects1[0], rects2[0],rects3[0]), ('RNN',"Bayesian memory","MLE"),ncol=3,fontsize=18)
plt.yticks(fontsize=20)
plt.ylim([0,24.5])
plt.xlim([-0.1,4.85])
plt.title("(b)   Median errors in 2D decoding task",fontsize=24,y=1.08, x=0.45)

plt.tight_layout()

autolabel(rects1)
autolabel(rects2)
autolabel(rects3)

# plt.savefig("median_erors.eps",format='eps')
# plt.savefig("median_errors.png",format='png')

plt.show()

1D decoding task

Figure 3.c, Matplotlib:

%use Python2

import matplotlib.pyplot as plt
import matplotlib.style
import matplotlib as mpl
mpl.style.use('classic')
import numpy as np
from scipy.io import loadmat
%matplotlib inline

# Bayesian results with flat prior
bay_track_dict_flat = loadmat("Bayes_res/Fig3cd_decodingDataForLinearizedT-mazeDiscard1st25s.mat")
# print bay_track_dict_flat.keys()
t_flat_medians = bay_track_dict_flat['medianErr']
t_flat_means = bay_track_dict_flat['meanErr']
t_flat_win = bay_track_dict_flat['tWin2Test'].flatten()
# print t_flat_win.shape, t_flat_means[:,0].shape
# print t_flat_win,t_flat_means[:,0]

# print("##################")
# print bay_track_dict_flat["bstMean"]
# print bay_track_dict_flat["bstMedian"]

# Bayesian results with memory
#bay_track_dict_history_old = loadmat("Bayes_res/1dDecodeFullBayesWithHistorySigma5History15.mat") # old params
bay_track_dict_history = loadmat("Bayes_res/Fig3cd_1dDecodeFullBayesWithHistorySigma5History15ExcludeFirst25.mat")

# print bay_track_dict_history.keys()
t_memory_medians = bay_track_dict_history['medianErr']
t_memory_means = bay_track_dict_history['meanErr']
t_memory_win = bay_track_dict_history['tWin2Test'].flatten()

# print np.min(t_memory_means,axis=0)
# print np.min(t_memory_medians,axis=0)


# print("##################")
# print bay_track_dict_history["bstMean"]
# print bay_track_dict_history["bstMedian"]


# Results with Bayes
# print np.min(t_memory_means, axis=0)
# print np.min(t_flat_means, axis=0)
# print np.min(t_memory_medians, axis=0)
# print np.min(t_flat_medians, axis=0)

#results in 1D

labels =["R2192", "R2198","R2336","R2337", "R2217"]

# Order of animals is changed compared to indexing in the .mat files
median = [7.09, 7.86, 7.19, 7.39, 12.21]
means = [ 14.508, 22.273, 22.724,21.087, 23.296]
mean_std = [3.7650, 1.292,3.287, 0.617, 2.003]
median_std =[0.37, 0.36, 0.46,0.49, 1.30] 

# Order of animals is changed compared to indexing in the .mat files
bayes_med = [8,8.00001,8,10,24] # 00001 is added to identify the value later in plotting
bayes_mean = [ 45.21788618, 52.29189189, 63.53548387, 69.39569892, 73.98571429]

# Order of animals is changed compared to indexing in the .mat files
memory_bayes_med = [8.001,10.001,8.001,10.001,24.001]
memory_bayes_mean = [ 44.11041667, 65.38104265, 59.35448718, 66.03916084, 75.1372093]

def autolabel(rects):
    # attach some text labels
    for i,rect in enumerate(rects):
        height = rect.get_height()
        col=tableau20[0]
        if height in memory_bayes_mean:
            col="black"
            ax.text(rect.get_x() + rect.get_width()/2., 1.0*height,
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)
        elif height in bayes_mean:
            col=tableau20[6]
            if height==  45.21788618:
                ax.text(rect.get_x() + rect.get_width()/2., 47,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            elif height== 52.29189189:
                ax.text(rect.get_x() + rect.get_width()/2., 69,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            elif height== 73.98571429:
                ax.text(rect.get_x() + rect.get_width()/2., 78.5,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            else:
                ax.text(rect.get_x() + rect.get_width()/2., 1.025*height,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
        else:
            #print mean_std[i//2]
            ax.text(rect.get_x() + rect.get_width()/2., 1.05*height+mean_std[i],
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)

fig, ax = plt.subplots(figsize=(8,6))
indx=np.arange(5)
width=0.3
rects1 = ax.bar(indx, means, width, color=tableau20[0], yerr=mean_std, error_kw={"linewidth":4, "ecolor":"black", "capsize":8,"capthick":2})
rects2 = ax.bar(indx + width+0.08, memory_bayes_mean, width/2, color=tableau20[6], hatch="///")
rects3 = ax.bar(indx + width*1.5+0.09, bayes_mean, width/2, color=tableau20[6])

# add some text for labels, title and axes ticks
plt.ylabel('Mean error (cm)',fontsize=22)
ax.set_xticks(indx + width+0.025)
ax.set_xticklabels(labels,fontsize=22)

ax.legend((rects1[0], rects2[0],rects3[0]), ('RNN',"Bayesian memory","MLE"),ncol=3,fontsize=18, loc=9)
plt.yticks(fontsize=20)
plt.ylim([0,100])
plt.xlim([-0.1,4.85])
plt.title("(c)   Mean errors in 1D decoding task",fontsize=24,y=1.08, x=0.45)

plt.tight_layout()

autolabel(rects1)
autolabel(rects2)
autolabel(rects3)

# plt.savefig("1D_mean_erors.eps",format='eps')
# plt.savefig("1D_mean_errors.png",format='png')

plt.show()

Figure 3.d, Matplotlib:

%use Python2

import matplotlib.pyplot as plt
import matplotlib.style
import matplotlib as mpl
mpl.style.use('classic')
import numpy as np
from scipy.io import loadmat
%matplotlib inline


# plotting of results in 1D

labels =["R2192", "R2198","R2336","R2337", "R2217"]

# results from RNN
median = [7.09, 7.86, 7.19, 7.39, 12.21]
means = [ 14.508, 22.273, 22.724,21.087, 23.296]
mean_std = [3.7650, 1.292, 6.9994, 0.62, 2.003]
median_std =[0.37, 0.36, 0.46, 0.49, 1.30] 

# Order of animals is changed compared to indexing in the .mat files
bayes_med = [8,8.00001,8,10,24] # 00001 is added to identify the value later in plotting
bayes_mean = [ 45.21788618, 52.29189189, 63.53548387, 69.39569892, 73.98571429]

# Order of animals is changed compared to indexing in the .mat files
memory_bayes_med = [8.001,10.001,8.001,10.001,24.001]
memory_bayes_mean = [ 44.11041667, 65.38104265, 59.35448718, 66.03916084, 75.1372093]

def autolabel(rects):
    # attach some text labels
    for i,rect in enumerate(rects):
        height = rect.get_height()
        col=tableau20[0]
        if height in memory_bayes_med:
            col="black"
            ax.text(rect.get_x() + rect.get_width()/2., 1.0*height,
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)
        elif height in bayes_med:
            col=tableau20[6]
            if height==8.00001:
                ax.text(rect.get_x() + rect.get_width()/2., 11.0,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            else:
                ax.text(rect.get_x() + rect.get_width()/2., np.max((9.0,1.0*height+0.9)),
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
        else:
            ax.text(rect.get_x() + rect.get_width()/2., 1.025*height + median_std[i],
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)

fig, ax = plt.subplots(figsize=(8,6))
indx=np.arange(5)
width=0.3
rects1 = ax.bar(indx, median, width, color=tableau20[0], yerr=median_std, error_kw={"linewidth":4, "ecolor":"black", "capsize":8,"capthick":2})
rects2 = ax.bar(indx + width+0.08, memory_bayes_med, width/2, color=tableau20[6], hatch="///")
rects3 = ax.bar(indx + width*1.5+0.09, bayes_med, width/2, color=tableau20[6])

# add some text for labels, title and axes ticks
plt.ylabel('Median error (cm)',fontsize=22)
ax.set_xticks(indx + width+0.025)
ax.set_xticklabels(labels,fontsize=22)

ax.legend((rects1[0], rects2[0],rects3[0]), ('RNN',"Bayesian memory", "MLE"),ncol=3,fontsize=18, loc=9)
plt.yticks(fontsize=20)
plt.ylim([0,30.0])
plt.xlim([-0.1,4.85])
plt.title("(d)   Median errors in 1D decoding task",fontsize=24,y=1.08, x=0.45)

autolabel(rects1)
autolabel(rects2)
autolabel(rects3)
plt.tight_layout()

#plt.savefig("1D_median_errors.eps",format='eps')
plt.savefig("1D_median_errors.png",format='png')
plt.show()

%use Python2
import pickle

with open('train.pickle', 'wb') as f:
    pickle.dump([means, mean_std, memory_bayes, bayes, labels, flat_means, order_of_animals, flat_medians, memory_means, memory_medians, tableau20], f)

Python 3.7

(a) Mean errors in 2D decoding task

Figure 3.a, Matplotlib:

%use Python3

import matplotlib.pyplot as plt
import matplotlib.style
import numpy as np
from scipy.io import loadmat
%matplotlib inline

with open('train.pickle', 'rb') as f:
    means, mean_std, memory_bayes, bayes, labels, flat_means, order_of_animals, flat_medians, memory_means, memory_medians, tableau20  = pickle.load(f, encoding='bytes')
    
labels =["R2192", "R2198","R2336","R2337", "R2217"]
order_of_animals = [0,1,3,4,2]
# results from RNN exctracted from log files and averaged
means = [12.50484, 13.27721, 16.28584545, 14.36655,  14.4936]
mean_std = [0.3846042705, 0.3226775496, 0.269230256, 0.2945978247,0.2544971159]

medians = [10.3296,10.75701,13.09772, 11.3265, 11.73398]
median_std =[0.23188, 0.31606,0.19988, 0.15216, 0.24249]

# Results from flat Bayes (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as it has least neurons
bayes =  np.min(flat_means, axis=0)[order_of_animals] #[ 15.82963073,  16.06929415, 18.81734775,  17.03691594,  17.86089428 ]
bayes_med =  np.min(flat_medians, axis=0)[order_of_animals]#[ 12.,   12.64911064 ,   14. ,  12.16552506, 14.14213562]

# Results from Bayes with memory (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as has least neurons
memory_bayes =  np.min(memory_means, axis=0)[order_of_animals] #[ 15.46168191,  14.99576142,  18.26098815,  16.40828295, 16.5269506] 
memory_bayes_med = np.min(memory_medians, axis=0)[order_of_animals] #[ 11.3137085,  12., 13.11132817,  12.16552506, 12.64911064]

#plot Bayes_flat, Bayes_memory and RNN
fig, ax = plt.subplots(figsize=(8,6))
indx=np.arange(5)
width=0.3
# RNN
rects1 = ax.bar(indx, means, width, color=tableau20[0], yerr=mean_std, error_kw={"linewidth":4, "ecolor":"black", "capsize":8,"capthick":2})
# Bayes mem
# rects2 = ax.bar(indx + width+0.08, memory_bayes, width/2, color=tableau20[6], hatch='///')
rects2 = ax.bar(indx + width+0.08, memory_bayes, width/2, color=tableau20[9])
# Bayes flat
rects3 = ax.bar(indx + width*1.5+0.09, bayes, width/2, color=tableau20[6])

# add some text for labels, title and axes ticks
plt.ylabel('Mean error (cm)',fontsize=22)
ax.set_xticks(indx + width+0.025)
ax.set_xticklabels(labels,fontsize=22)
ax.legend((rects1[0], rects2[0], rects3[0]), ('RNN',"Bayesian memory", "MLE"),ncol=3,fontsize=18)

plt.yticks(fontsize=20)

plt.ylim([0,24.5])
plt.xlim([-0.20,4.75])
plt.title("(a)   Mean errors in 2D decoding task",fontsize=24,y=1.08, x=0.45)

def autolabel(rects):
    # attach some text labels
    for i,rect in enumerate(rects):
        height = rect.get_height()
        col=tableau20[0]
        if height in memory_bayes:
            col="black"
            ax.text(rect.get_x() + rect.get_width()/2., 1.0*height,
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)
        elif height in bayes:
            col=tableau20[6]
            ax.text(rect.get_x() + rect.get_width()/2., 1.025*height+0.5,
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)
        else:
            ax.text(rect.get_x() + rect.get_width()/2., 1.05*height+mean_std[i],
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)
autolabel(rects1)
autolabel(rects2)
autolabel(rects3)

plt.tight_layout()

# plt.savefig("mean_erors.eps",format='eps')
# plt.savefig("mean_errors.png",format='png')
plt.show()

Figure 3.a, Plotly:

%use Python3
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from IPython.core.display import display, HTML
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}

with open('train.pickle', 'rb') as f:
    means, mean_std, memory_bayes, bayes, labels, flat_means, order_of_animals, flat_medians, memory_means, memory_medians, tableau20  = pickle.load(f, encoding='bytes')
    
labels =["R2192", "R2198","R2336","R2337", "R2217"]
order_of_animals = [0,1,3,4,2]
# results from RNN exctracted from log files and averaged
means = [12.50484, 13.27721, 16.28584545, 14.36655,  14.4936]
mean_std = [0.3846042705, 0.3226775496, 0.269230256, 0.2945978247,0.2544971159]

medians = [10.3296,10.75701,13.09772, 11.3265, 11.73398]
median_std =[0.23188, 0.31606,0.19988, 0.15216, 0.24249]

# Results from flat Bayes (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as it has least neurons
bayes =  np.min(flat_means, axis=0)[order_of_animals] #[ 15.82963073,  16.06929415, 18.81734775,  17.03691594,  17.86089428 ]
bayes_med =  np.min(flat_medians, axis=0)[order_of_animals]#[ 12.,   12.64911064 ,   14. ,  12.16552506, 14.14213562]

# Results from Bayes with memory (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as has least neurons
memory_bayes =  np.min(memory_means, axis=0)[order_of_animals] #[ 15.46168191,  14.99576142,  18.26098815,  16.40828295, 16.5269506] 
memory_bayes_med = np.min(memory_medians, axis=0)[order_of_animals] #[ 11.3137085,  12., 13.11132817,  12.16552506, 12.64911064]

figa = go.Figure()
indx=np.arange(5)
width_0=0.3

figa.add_trace(go.Bar(
    x=labels,
    y=means,
    error_y = dict(array = mean_std,
                   color = 'black', 
                   width = 4, 
                   thickness = 3,
                   value = 8
                   ),
    name='RNN',
    marker_color="rgb"+str(tableau20[0]), 
    width = width_0,
    offset = -0.4,
    text = means,
    textfont=dict(
        size=14,
        color="rgb"+str(tableau20[0])
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))

figa.add_trace(go.Bar(
    x=labels,
    y=memory_bayes,
    name='Bayesian Memory',
    marker_color="rgb"+str(tableau20[9]),
    width = width_0 / 1.5,
    offset = 0.0,
    text = memory_bayes,
    textfont=dict(
        size=18,
        color="black"
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))

figa.add_trace(go.Bar(
    x=labels,
    y=bayes,
    name='MLE',
    marker_color="rgb"+str(tableau20[6]),
    width = width_0 / 1.5,
    offset = 0.205,
    text = bayes,
    textfont=dict(
        size=18,
        color="rgb"+str(tableau20[6])
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))


figa.update_layout(title = '(a)   Mean errors in 2D decoding task',
                  title_x = 0.5, 
                  xaxis=dict(range=[-0.45,4.5], 
                             mirror='all',
                             ticks='outside',
                             showline=True,
                             linecolor='#000',       
                             tickfont = dict(size=20)),
                  yaxis_title='Mean Error (cm)',
                  yaxis=dict(range=[0,24.5], 
                             mirror=True,
                             ticks='outside', 
                             showline=True, 
                             linecolor='#000',
                             tickfont = dict(size=20)),
                  legend=dict(orientation = 'h',
                              x = 0.25, 
                              y = 1,
                              bordercolor="Gray",
                              borderwidth=1),
                  plot_bgcolor='#fff', 
                  width = 800, 
                  height = 500,
                  font = dict(size = 17),
                  margin=go.layout.Margin(l=50,
                                         r=50,
                                         b=60,
                                        t=35),
                   bargap=0.25,
                   bargroupgap=0.1)

figa.update_traces(texttemplate='%{text:.2f}', textposition='outside')

plot(figa, filename = 'fig3_a.html', config = config)
# THEBELAB
display(HTML('fig3_a.html'))
# BINDER
# iplot(figa,config=config)

(b) Median errorsin 2D decoding task

Figure 3.b, Matplotlib

%use Python3

import matplotlib.pyplot as plt
import matplotlib.style
# import matplotlib as mpl
# mpl.style.use('classic')
import numpy as np
from scipy.io import loadmat
%matplotlib inline

with open('train.pickle', 'rb') as f:
    means, mean_std, memory_bayes, bayes, labels, flat_means, order_of_animals, flat_medians, memory_means, memory_medians, tableau20  = pickle.load(f, encoding='bytes')
    
labels =["R2192", "R2198","R2336","R2337", "R2217"]
#results from RNN
means = [12.50484, 13.27721, 16.28584545, 14.36655,  14.4936]
mean_std = [0.3846042705, 0.3226775496, 0.269230256, 0.2945978247,0.2544971159]
medians = [10.3296,10.75701,13.09772, 11.3265, 11.73398]
median_std =[0.23188, 0.31606,0.19988, 0.15216, 0.24249]

# Results from flat Bayes (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as it has least neurons
bayes =  np.min(flat_means, axis=0)[order_of_animals] #[ 15.82963073,  16.06929415, 18.81734775,  17.03691594,  17.86089428 ]
bayes_med =  np.min(flat_medians, axis=0)[order_of_animals]#[ 12.,   12.64911064 ,   14. ,  12.16552506, 14.14213562]

# Results from Bayes with memory (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as has least neurons
memory_bayes =  np.min(memory_means, axis=0)[order_of_animals] #[ 15.46168191,  14.99576142,  18.26098815,  16.40828295, 16.5269506] 
memory_bayes_med = np.min(memory_medians, axis=0)[order_of_animals] #[ 11.3137085,  12., 13.11132817,  12.16552506, 12.64911064]


#plot Bayes_flat, Bayes_memory and RNN
fig, ax = plt.subplots(figsize=(8,6))
indx=np.arange(5)
width=0.3
# RNN, flat, memory
rects1 = ax.bar(indx, medians, width, color=tableau20[0], yerr=median_std, error_kw={"linewidth":4, "ecolor":"black", "capsize":8,"capthick":2})
rects2 = ax.bar(indx + width+0.08, memory_bayes_med, width/2, color=tableau20[9])
rects3 = ax.bar(indx + width*1.5+0.09, bayes_med, width/2, color=tableau20[6])

# add some text for labels, title and axes ticks
plt.ylabel('Median error (cm)',fontsize=22)
ax.set_xticks(indx + width+0.025)
ax.set_xticklabels(labels,fontsize=22)

ax.legend((rects1[0], rects2[0],rects3[0]), ('RNN',"Bayesian memory","MLE"),ncol=3,fontsize=18)
plt.yticks(fontsize=20)
plt.ylim([0,24.5])
plt.xlim([-0.20,4.75])
plt.title("(b)   Median errors in 2D decoding task",fontsize=24,y=1.08, x=0.45)


def autolabel(rects):
    # attach some text labels
    for i,rect in enumerate(rects):
        height = rect.get_height()
        col=tableau20[0]
        if height in memory_bayes_med:
            col="black"
            if height== 13.1113:
                ax.text(rect.get_x() + rect.get_width()/2., 1.02*height+0.4,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            else:
                ax.text(rect.get_x() + rect.get_width()/2., 1.02*height,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
        elif height in bayes_med:
            col=tableau20[6]
            if height== 12.16552506:
                ax.text(rect.get_x() + rect.get_width()/2., 1.0*height+1.6,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            else:
                ax.text(rect.get_x() + rect.get_width()/2., 1.0*height+0.6,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
        else:
            ax.text(rect.get_x() + rect.get_width()/2., 1.0*height+median_std[i],
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)

autolabel(rects1)
autolabel(rects2)
autolabel(rects3)
plt.tight_layout()

#plt.savefig("median_erors.eps",format='eps')
plt.savefig("median_errors.png",format='png')
plt.show()

Figure 3.b, Plotly:

%use Python3
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from IPython.core.display import display, HTML
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}

with open('train.pickle', 'rb') as f:
    means, mean_std, memory_bayes, bayes, labels, flat_means, order_of_animals, flat_medians, memory_means, memory_medians, tableau20  = pickle.load(f, encoding='bytes')

    
labels =["R2192", "R2198","R2336","R2337", "R2217"]
#results from RNN
means = [12.50484, 13.27721, 16.28584545, 14.36655,  14.4936]
mean_std = [0.3846042705, 0.3226775496, 0.269230256, 0.2945978247,0.2544971159]
medians = [10.3296,10.75701,13.09772, 11.3265, 11.73398]
median_std =[0.23188, 0.31606,0.19988, 0.15216, 0.24249]

# Results from flat Bayes (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as it has least neurons
bayes =  np.min(flat_means, axis=0)[order_of_animals] #[ 15.82963073,  16.06929415, 18.81734775,  17.03691594,  17.86089428 ]
bayes_med =  np.min(flat_medians, axis=0)[order_of_animals]#[ 12.,   12.64911064 ,   14. ,  12.16552506, 14.14213562]

# Results from Bayes with memory (see the cell above), notice that order of animals is different, 
# we move R2217 to the end as has least neurons
memory_bayes =  np.min(memory_means, axis=0)[order_of_animals] #[ 15.46168191,  14.99576142,  18.26098815,  16.40828295, 16.5269506] 
memory_bayes_med = np.min(memory_medians, axis=0)[order_of_animals] #[ 11.3137085,  12., 13.11132817,  12.16552506, 12.64911064]


figb = go.Figure()
indx=np.arange(5)
width_0=0.3

figb.add_trace(go.Bar(
    x=labels,
    y=medians,
    error_y = dict(array = median_std,
                   color = 'black', 
                   width = 4, 
                   thickness = 3,
                   value = 8
                   ),
    name='RNN',
    marker_color="rgb"+str(tableau20[0]), 
    width = width_0,
    offset = -0.4,
    text = medians,
    textfont=dict(
        size=14,
        color="rgb"+str(tableau20[0])
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))

figb.add_trace(go.Bar(
    x=labels,
    y=memory_bayes_med,
    name='Bayesian Memory',
    marker_color="rgb"+str(tableau20[9]),
    width = width_0 / 1.5,
    offset = 0.0,
    text = memory_bayes_med,
    textfont=dict(
        size=18,
        color="black"
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))

figb.add_trace(go.Bar(
    x=labels,
    y=bayes_med,
    name='MLE',
    marker_color="rgb"+str(tableau20[6]),
    width = width_0 / 1.5,
    offset = 0.205,
    text = bayes_med,
    textfont=dict(
        size=18,
        color="rgb"+str(tableau20[6])
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))


figb.update_layout(title = '(b)   Median errors in 2D decoding task',
                  title_x = 0.5, 
                  xaxis=dict(range=[-0.45,4.5], 
                             mirror='all',
                             ticks='outside',
                             showline=True,
                             linecolor='#000',       
                             tickfont = dict(size=20)),
                  yaxis_title='Mean Error (cm)',
                  yaxis=dict(range=[0,24.5], 
                             mirror=True,
                             ticks='outside', 
                             showline=True, 
                             linecolor='#000',
                             tickfont = dict(size=20)),
                  legend=dict(orientation = 'h',
                              x = 0.25, 
                              y = 1,
                              bordercolor="Gray",
                              borderwidth=1),
                  plot_bgcolor='#fff', 
                  width = 800, 
                  height = 500,
                  font = dict(size = 17),
                  margin=go.layout.Margin(l=50,
                                         r=50,
                                         b=60,
                                        t=35),
                   bargap=0.25,
                   bargroupgap=0.1)

figb.update_traces(texttemplate='%{text:.2f}', textposition='outside')

plot(figb, filename = 'fig3_b.html', config = config)
# THEBELAB
display(HTML('fig3_b.html'))
# BINDER
# iplot(figb,config=config)

(c) Mean errors in 1D decoding task

Figure 3.c, Matplotlib:

%use Python3

import matplotlib.pyplot as plt
import matplotlib.style
# import matplotlib as mpl
# mpl.style.use('classic')
import numpy as np
from scipy.io import loadmat
%matplotlib inline


with open('train.pickle', 'rb') as f:
    means, mean_std, memory_bayes, bayes, labels, flat_means, order_of_animals, flat_medians, memory_means, memory_medians, tableau20  = pickle.load(f, encoding='bytes')
    
    
#results in 1D
labels =["R2192", "R2198","R2336","R2337", "R2217"]

# Order of animals is changed compared to indexing in the .mat files
median = [7.09, 7.86, 7.19, 7.39, 12.21]
means = [ 14.508, 22.273, 22.724,21.087, 23.296]
mean_std = [3.7650, 1.292,3.287, 0.617, 2.003]
median_std =[0.37, 0.36, 0.46,0.49, 1.30] 

# Order of animals is changed compared to indexing in the .mat files
bayes_med = [8,8.00001,8,10,24] # 00001 is added to identify the value later in plotting
bayes_mean = [ 45.21788618, 52.29189189, 63.53548387, 69.39569892, 73.98571429]

# Order of animals is changed compared to indexing in the .mat files
memory_bayes_med = [8.001,10.001,8.001,10.001,24.001]
memory_bayes_mean = [ 44.11041667, 65.38104265, 59.35448718, 66.03916084, 75.1372093]

fig, ax = plt.subplots(figsize=(8,6))
indx=np.arange(5)
width=0.3
rects1 = ax.bar(indx, means, width, color=tableau20[0], yerr=mean_std, error_kw={"linewidth":4, "ecolor":"black", "capsize":8,"capthick":2})
# rects2 = ax.bar(indx + width+0.08, memory_bayes_mean, width/2, color=tableau20[6], hatch="///")
rects2 = ax.bar(indx + width+0.08, memory_bayes_mean, width/2, color=tableau20[9])
rects3 = ax.bar(indx + width*1.5+0.09, bayes_mean, width/2, color=tableau20[6])

# add some text for labels, title and axes ticks
plt.ylabel('Mean error (cm)',fontsize=22)
ax.set_xticks(indx + width+0.025)
ax.set_xticklabels(labels,fontsize=22)

ax.legend((rects1[0], rects2[0],rects3[0]), ('RNN',"Bayesian memory","MLE"),ncol=3,fontsize=18, loc=9)
plt.yticks(fontsize=20)
plt.ylim([0,100])
plt.xlim([-0.20,4.75])
plt.title("(c)   Mean errors in 1D decoding task",fontsize=24,y=1.08, x=0.45)


def autolabel(rects):
    # attach some text labels
    for i,rect in enumerate(rects):
        height = rect.get_height()
        col=tableau20[0]
        if height in memory_bayes_mean:
            col="black"
            ax.text(rect.get_x() + rect.get_width()/2., 1.0*height,
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)
        elif height in bayes_mean:
            col=tableau20[6]
            if height==  45.21788618:
                ax.text(rect.get_x() + rect.get_width()/2., 47,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            elif height== 52.29189189:
                ax.text(rect.get_x() + rect.get_width()/2., 69,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            elif height== 73.98571429:
                ax.text(rect.get_x() + rect.get_width()/2., 78.5,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            else:
                ax.text(rect.get_x() + rect.get_width()/2., 1.025*height,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
        else:
            #print mean_std[i//2]
            ax.text(rect.get_x() + rect.get_width()/2., 1.05*height+mean_std[i],
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)
autolabel(rects1)
autolabel(rects2)
autolabel(rects3)
plt.tight_layout()

#plt.savefig("1D_mean_erors.eps",format='eps')
plt.savefig("1D_mean_errors.png",format='png')
plt.show()

Figure 3.c, Plotly:

%use Python3
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from IPython.core.display import display, HTML
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}

with open('train.pickle', 'rb') as f:
    means, mean_std, memory_bayes, bayes, labels, flat_means, order_of_animals, flat_medians, memory_means, memory_medians, tableau20  = pickle.load(f, encoding='bytes')

     
#results in 1D
labels =["R2192", "R2198","R2336","R2337", "R2217"]

# Order of animals is changed compared to indexing in the .mat files
median = [7.09, 7.86, 7.19, 7.39, 12.21]
means = [ 14.508, 22.273, 22.724,21.087, 23.296]
mean_std = [3.7650, 1.292,3.287, 0.617, 2.003]
median_std =[0.37, 0.36, 0.46,0.49, 1.30] 

# Order of animals is changed compared to indexing in the .mat files
bayes_med = [8,8.00001,8,10,24] # 00001 is added to identify the value later in plotting
bayes_mean = [ 45.21788618, 52.29189189, 63.53548387, 69.39569892, 73.98571429]

# Order of animals is changed compared to indexing in the .mat files
memory_bayes_med = [8.001,10.001,8.001,10.001,24.001]
memory_bayes_mean = [ 44.11041667, 65.38104265, 59.35448718, 66.03916084, 75.1372093]


figc = go.Figure()
indx=np.arange(5)
width_0=0.3

figc.add_trace(go.Bar(
    x=labels,
    y=means,
    error_y = dict(array = mean_std,
                   color = 'black', 
                   width = 4, 
                   thickness = 3,
                   value = 8
                   ),
    name='RNN',
    marker_color="rgb"+str(tableau20[0]), 
    width = width_0,
    offset = -0.4,
    text = means,
    textfont=dict(
        size=14,
        color="rgb"+str(tableau20[0])
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))

figc.add_trace(go.Bar(
    x=labels,
    y=memory_bayes_mean,
    name='Bayesian Memory',
    marker_color="rgb"+str(tableau20[9]),
    width = width_0 / 1.5,
    offset = 0.0,
    text = memory_bayes_mean,
    textfont=dict(
        size=18,
        color="black"
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))

figc.add_trace(go.Bar(
    x=labels,
    y=bayes_mean,
    name='MLE',
    marker_color="rgb"+str(tableau20[6]),
    width = width_0 / 1.5,
    offset = 0.205,
    text = bayes_mean,
    textfont=dict(
        size=18,
        color="rgb"+str(tableau20[6])
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))


figc.update_layout(title = '(c)   Mean errors in 1D decoding task',
                  title_x = 0.5, 
                  xaxis=dict(range=[-0.45,4.5], 
                             mirror='all',
                             ticks='outside',
                             showline=True,
                             linecolor='#000',       
                             tickfont = dict(size=20)),
                  yaxis_title='Mean Error (cm)',
                  yaxis=dict(range=[0,90], 
                             mirror=True,
                             ticks='outside', 
                             showline=True, 
                             linecolor='#000',
                             tickfont = dict(size=20)),
                  legend=dict(orientation = 'h',
                              x = 0.25, 
                              y = 1,
                              bordercolor="Gray",
                              borderwidth=1),
                  plot_bgcolor='#fff', 
                  width = 800, 
                  height = 500,
                  font = dict(size = 17),
                  margin=go.layout.Margin(l=50,
                                         r=50,
                                         b=60,
                                        t=35),
                   bargap=0.25,
                   bargroupgap=0.1)

figc.update_traces(texttemplate='%{text:.2f}', textposition='outside')

plot(figc, filename = 'fig3_c.html', config = config)
# THEBELAB
display(HTML('fig3_c.html'))
# BINDER
# iplot(figc,config=config)

(d) Median errors in 1D decoding task

Figure 3.d, Matplotlib:

%use Python3

import matplotlib.pyplot as plt
import matplotlib.style
# import matplotlib as mpl
# mpl.style.use('classic')
import numpy as np
from scipy.io import loadmat
%matplotlib inline


with open('train.pickle', 'rb') as f:
    means, mean_std, memory_bayes, bayes, labels, flat_means, order_of_animals, flat_medians, memory_means, memory_medians, tableau20  = pickle.load(f, encoding='bytes')

#results in 1D
labels =["R2192", "R2198","R2336","R2337", "R2217"]
    
# results from RNN
median = [7.09, 7.86, 7.19, 7.39, 12.21]
means = [ 14.508, 22.273, 22.724,21.087, 23.296]
mean_std = [3.7650, 1.292, 6.9994, 0.62, 2.003]
median_std =[0.37, 0.36, 0.46, 0.49, 1.30] 

# Order of animals is changed compared to indexing in the .mat files
bayes_med = [8,8.00001,8,10,24] # 00001 is added to identify the value later in plotting
bayes_mean = [ 45.21788618, 52.29189189, 63.53548387, 69.39569892, 73.98571429]

# Order of animals is changed compared to indexing in the .mat files
memory_bayes_med = [8.001,10.001,8.001,10.001,24.001]
memory_bayes_mean = [ 44.11041667, 65.38104265, 59.35448718, 66.03916084, 75.1372093]

fig, ax = plt.subplots(figsize=(8,6))
indx=np.arange(5)
width=0.3
rects1 = ax.bar(indx, median, width, color=tableau20[0], yerr=median_std, error_kw={"linewidth":4, "ecolor":"black", "capsize":8,"capthick":2})
# rects2 = ax.bar(indx + width+0.08, memory_bayes_med, width/2, color=tableau20[6], hatch="///")
rects2 = ax.bar(indx + width+0.08, memory_bayes_med, width/2, color=tableau20[9])
rects3 = ax.bar(indx + width*1.5+0.09, bayes_med, width/2, color=tableau20[6])

# add some text for labels, title and axes ticks
plt.ylabel('Median error (cm)',fontsize=22)
ax.set_xticks(indx + width+0.025)
ax.set_xticklabels(labels,fontsize=22)

ax.legend((rects1[0], rects2[0],rects3[0]), ('RNN',"Bayesian memory", "MLE"),ncol=3,fontsize=18, loc=9)
plt.yticks(fontsize=20)
plt.ylim([0,30])
plt.xlim([-0.20,4.75])
plt.title("(d)   Median errors in 1D decoding task",fontsize=24,y=1.08, x=0.45)


def autolabel(rects):
    # attach some text labels
    for i,rect in enumerate(rects):
        height = rect.get_height()
        col=tableau20[0]
        if height in memory_bayes_med:
            col="black"
            ax.text(rect.get_x() + rect.get_width()/2., 1.0*height,
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)
        elif height in bayes_med:
            col=tableau20[6]
            if height==8.00001:
                ax.text(rect.get_x() + rect.get_width()/2., 11.0,
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
            else:
                ax.text(rect.get_x() + rect.get_width()/2., np.max((9.0,1.0*height+0.9)),
                    '%.2f' % height,
                    ha='center', va='bottom',color=col,weight="bold", size=14)
        else:
            ax.text(rect.get_x() + rect.get_width()/2., 1.025*height + median_std[i],
                '%.2f' % height,
                ha='center', va='bottom',color=col,weight="bold", size=14)

plt.tight_layout()            
autolabel(rects1)
autolabel(rects2)
autolabel(rects3)


#plt.savefig("1D_median_errors.eps",format='eps')
plt.savefig("1D_median_errors.png",format='png')
plt.show()

Figure 3.d, Plotly:

%use Python3
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from IPython.core.display import display, HTML
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}
    
labels =["R2192", "R2198","R2336","R2337", "R2217"]

# results from RNN
median = [7.09, 7.86, 7.19, 7.39, 12.21]
means = [ 14.508, 22.273, 22.724,21.087, 23.296]
mean_std = [3.7650, 1.292, 6.9994, 0.62, 2.003]
median_std =[0.37, 0.36, 0.46, 0.49, 1.30] 

# Order of animals is changed compared to indexing in the .mat files
bayes_med = [8,8.00001,8,10,24] # 00001 is added to identify the value later in plotting
bayes_mean = [ 45.21788618, 52.29189189, 63.53548387, 69.39569892, 73.98571429]

# Order of animals is changed compared to indexing in the .mat files
memory_bayes_med = [8.001,10.001,8.001,10.001,24.001]
memory_bayes_mean = [ 44.11041667, 65.38104265, 59.35448718, 66.03916084, 75.1372093]

figd = go.Figure()

width_0=0.3

figd.add_trace(go.Bar(
    x=labels,
    y=median,
    error_y = dict(array = median_std,
                   color = 'black', 
                   width = 4, 
                   thickness = 3,
                   value = 8
                   ),
    name='RNN',
    marker_color="rgb"+str(tableau20[0]), 
    width = width_0,
    offset = -0.4,
    text = median,
    textfont=dict(
        size=14,
        color="rgb"+str(tableau20[0])
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))

figd.add_trace(go.Bar(
    x=labels,
    y=memory_bayes_med,
    name='Bayesian Memory',
    marker_color="rgb"+str(tableau20[9]),
    width = width_0 / 1.5,
    offset = 0.0,
    text = memory_bayes_med,
    textfont=dict(
        size=18,
        color="black"
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))

figd.add_trace(go.Bar(
    x=labels,
    y=bayes_med,
    name='MLE',
    marker_color="rgb"+str(tableau20[6]),
    width = width_0 / 1.5,
    offset = 0.205,
    text = bayes_med,
    textfont=dict(
        size=18,
        color="rgb"+str(tableau20[6])
    ),
    hovertemplate = '<b> Label: </b> <i> %{x} </i>, <br> <b> Mean Error: </b> <i> %{y: .2f} cm </i>'
))


figd.update_layout(title = '(d)   Median errors in 1D decoding task',
                  title_x = 0.5, 
                  xaxis=dict(range=[-0.45,4.5], 
                             mirror='all',
                             ticks='outside',
                             showline=True,
                             linecolor='#000',       
                             tickfont = dict(size=20)),
                  yaxis_title='Mean Error (cm)',
                  yaxis=dict(range=[0,30.5], 
                             mirror=True,
                             ticks='outside', 
                             showline=True, 
                             linecolor='#000',
                             tickfont = dict(size=20)),
                  legend=dict(orientation = 'h',
                              x = 0.25, 
                              y = 1,
                              bordercolor="Gray",
                              borderwidth=1),
                  plot_bgcolor='#fff', 
                  width = 800, 
                  height = 500,
                  font = dict(size = 17),
                  margin=go.layout.Margin(l=50,
                                         r=50,
                                         b=60,
                                        t=35),
                   bargap=0.25,
                   bargroupgap=0.1)

figd.update_traces(texttemplate='%{text:.2f}', textposition='outside')

plot(figd, filename = 'fig3_d.html', config = config)
# THEBELAB
display(HTML('fig3_d.html'))
# BINDER
# iplot(figd,config=config)